DBot Create Phishing Classifier V2

Creates a phishing classifier using machine learning technique, based on the email content.

Dependencies

This playbook uses the following sub-playbooks, integrations, and scripts.

Sub-playbooks

This playbook does not use any sub-playbooks.

Integrations

This playbook does not use any integrations.

Scripts

  • GetIncidentsByQuery
  • Base64ListToFile
  • DBotPreProcessTextData
  • DBotTrainTextClassifierV2

Commands

This playbook does not use any commands.

Playbook Inputs


NameDescriptionDefault ValueSourceRequired
modelNameThe model name to store in the system.phishing_model-Optional
emailTextKeyThe CSV list of incident fields names with the email body or html body. You can also use "" if you want to choose the first non-empty value from a list of fields.emailbodyemailbodyhtml
emailSubjectKeyThe CSV list of incident fields names with the email subject. You can also use "" if you want to choose the first non-empty value from a list of fields.emailsubjectname
emailTagKeyThe field name with the email tag. Supports a CSV list, the first non-empty value will be taken.emailclassification-Optional
phishingLabelsThe CSV list of email tags values and mapping. The script considers only the tags specified in this field. You can map label to another value by using this format: LABEL:MAPPED_LABEL. For example, for 4 values in email tag: malicious, credentials harvesting, inner communitcation, external legit email, unclassified. While training, we want to ignore "unclassified" tag, and refer to "credentials harvesting" as "malicious" too. Also, we want to merge "inner communitcation" and "external legit email" to one tag called "non-malicious". The input will be: malicious, credentials harvesting:malicious, inner communitcation:non-malicious, external legit email:non-malicious*-Optional
incidentsQueryThe incidents query to fetch the training data for the model.--Optional
maxIncidentsToFetchOnTrainingThe maximum number of incidents to fetch.3000-Optional
hashSeedIf non-empty, hash every word with this seed.--Optional
historicalDataFileListNameThe name of demisto list contains historical data training samples for the model.--Optional
overrideModelWhether to override the existing model if a model with the same name exists. The default is "false".true-Optional
incidentTypesThe CSV list of incident types by which to filter.Phishing-Optional
dedupThresholdRemoves emails with similarity greater then this threshold. The range 0-1, where 1 is completly identical.0.99-Optional
removeShortTextThresholdThe sample text of which the total number words are less than or equal to this number will be ignored.15-Optional
modelTargetAccuracyThe model target accuracy, between 0 and 1.0.8-Optional
outputFormatThe output file format. Can be "json" or "pickle".pickle-Optional

Playbook Outputs


PathDescriptionType
DBotPhishingClassifier.EvaluationScores.Precision.AllThe average binary precision over all classes (0-1).number
DBotPhishingClassifier.EvaluationScores.TP.AllThe number of instances of all classes that were predicted correctly.number
DBotPhishingClassifier.EvaluationScores.Coverage.AllThe number of instances that were predicted at a probability greater than the threshold.number
DBotPhishingClassifier.EvaluationScores.Total.AllThe total number of instances.number
DBotPhishingClassifier.ModelNameThe name of the model in Demisto.string

Playbook Image


DBot_Create_Phishing_Classifier_V2